What Are Time Series Databases, and Why Do You Need Them?
The use of time series databases (TSDBs) has been prevalent in various industries for decades, particularly in finance and process-control systems. However, if you think you’ve been hearing more about them lately, you’re probably right.
Additionally, as IT infrastructure continues to expand, monitoring data from various sources such as servers, network devices, and microservices generates massive amounts of time series data, further highlighting the need for modern TSDBs.
While legacy time series solutions exist, many suffer from outdated architectures, limited scalability and integration challenges with modern data-analysis tools. Consequently, a new generation of time series databases has emerged, with over 20 new TSDBs being released in the past decade, particularly open source solutions.
These new TSDBs feature modern architectures that enable distributed processing and horizontal scaling, with open APIs that facilitate integration with data analysis tools and flexible deployment options in the cloud or on-premises.
Ensuring data quality and accuracy is critical for making data-driven decisions. Time series data can be “noisy” and contain missing or corrupted values. Time series databases often provide tools for cleaning and filtering data, as well as methods for detecting anomalies and outliers.
Here, we’ll explore the growing popularity of time series databases, the challenges associated with legacy solutions, and the features and benefits of modern TSDBs that make them an ideal choice for handling and analyzing vast amounts of time series data.
What Is Time Series Data?
First, let’s take a step back. What is time series data?
Time series data is data that is characterized by its time-based nature, where each datapoint is associated with a timestamp that indicates when it was recorded. This is in contrast to other types of data (such as transactional or document-based), which may not have a timestamp or may not be organized in a time-based sequence.
As mentioned earlier, one of the main reasons why time series data is becoming more prevalent is the rise of IoT devices and other sources of streaming data. IoT devices, such as sensors and connected devices, often generate large volumes of time-stamped data that need to be collected and analyzed in real time.
For example, a smart building might collect data on temperature, humidity and occupancy, while a manufacturing plant might collect data on machine performance and product quality.
Similarly, cloud computing and big data technologies have made it easier to store and process large volumes of time series data, enabling organizations to extract insights and make data-driven decisions more quickly.
Another factor contributing to the prevalence of time series data is the increasing use of machine learning and artificial intelligence, which often rely on time series data to make predictions and detect anomalies.
For example, a machine learning model might be trained on time series data from a sensor network to predict when equipment is likely to fail or to detect when environmental conditions are outside of normal ranges.
How Is Time Series Data Used?
There are many applications and industries that commonly use time series data, including:
IoT and smart buildings. Smart buildings use sensors to collect time series data on environmental factors such as temperature, humidity and occupancy, as well as energy usage and equipment performance. This data is used to optimize building operations, reduce energy costs and improve occupant comfort.
Manufacturing. Manufacturing plants collect time series data on machine performance, product quality and supply chain logistics to improve efficiency, reduce waste and ensure quality control.
Retail. Retailers use time series data to track sales, inventory levels and customer behavior. This data is used to optimize pricing, inventory management and marketing strategies.
Telecommunications. Telecommunications companies use time series data to monitor network performance, identify issues and optimize capacity. This data includes information on call volume, network traffic and equipment performance.
Finance. Financial trading firms use time series data to track stock prices, trading volumes and other financial metrics. This data is used to make investment decisions, monitor market trends and develop trading algorithms.
Health care. Health care providers use time series data to monitor patient vitals, track disease outbreaks and identify trends in patient outcomes. This data is used to develop treatment plans, improve patient care and support public health initiatives.
Transportation and logistics. Transportation and logistics companies use time series data to track vehicle and shipment locations, monitor supply chain performance and optimize delivery routes.
Energy. The energy industry relies on time series data to monitor and optimize power generation, transmission and distribution. This data includes information on energy consumption, weather patterns and equipment performance.
In each of these industries, time series data is critical for real-time monitoring and decision-making. It allows organizations to detect anomalies, optimize processes and make data-driven decisions that improve efficiency, reduce costs and increase revenue.
Time Series vs. Traditional Databases
Applications that collect and analyze time series data face specific challenges that are not encountered in traditional data storage and analytics.
Time series databases offer several advantages over traditional databases and other storage solutions when it comes to handling time series data. Here are some of the key advantages:
High Write Throughput
Time series databases are designed to handle large volumes of incoming data points that arrive at a high frequency. They are optimized for high write throughput, which means they can ingest and store large amounts of data quickly and efficiently, in real time. This is essential for applications that generate a lot of streaming data, such as IoT devices or log files.
For example, an IoT application that collects sensor data from thousands of devices every second requires a TSDB that can handle a high volume of writes and provide high availability to ensure data is not lost.
Traditional databases, on the other hand, are designed for transactional workloads and are not optimized for write-intensive workloads.
Automatic Downsampling and Compression
Time series data is high-dimensional — meaning it has multiple attributes or dimensions, such as time, location, and sensor values. It can quickly consume large amounts of storage space. Storing and retrieving this data efficiently can require a significant amount of disk space and computational resources.
TSDBs often use downsampling and compression techniques to reduce storage requirements, providing more efficient storage without sacrificing accuracy. This makes it more cost-effective to store large volumes of time series data.
Downsampling involves aggregating multiple data points into a single data point at a coarser granularity, while compression involves reducing the size of the data by removing redundant information. These techniques help to reduce storage costs and improve query performance, as less data needs to be queried.
Specialized Query Languages
TSDBs often offer specialized query languages that are optimized for time series data.
These languages, such as InfluxQL, support common time-based operations such as windowing, filtering, and aggregation, and can perform these operations efficiently on large datasets. This makes it easier to extract insights from time series data and perform real-time analytics.
Time series databases use time-based indexing to organize data based on timestamps, which allows for fast and efficient retrieval of data based on time ranges. This is important for applications that need to respond to events as they happen.
For instance: A financial trading firm needs to be able to respond to changes in the market quickly to take advantage of opportunities. This requires a time series database that can provide fast query response times and support real-time analytics.
Traditional databases may not have this level of indexing, which can make querying time-based data slower and less efficient.
Data Retention Policies
Time series databases offer features for managing data retention policies, such as automatically deleting or archiving old data after a certain period. This is important for managing storage costs and ensuring that data is kept only for as long as it is necessary.
Scalability and High Availability
Scalability and high availability are critical for handling large volumes of time series data. TSDBs are designed to scale horizontally across multiple nodes, which enables users to handle increasing data volumes and user demands. Along with automatic load balancing and failover, these features are critical for supporting high-performance, real-time analytics on time series data.
TSDBs can also provide high availability, which ensures that data is always accessible and not lost due to hardware failures or other issues.
5 Popular Open Source Time Series Databases
There are several open source TSDBs available in the market today. These databases offer flexibility, scalability, and cost-effectiveness, making them an attractive option for organizations looking to handle and analyze vast amounts of time series data.
These open source TSDBs are constantly evolving and improving, and each has its own strengths and weaknesses. Choosing the right one depends on the specific use case, data requirements and your organization’s IT infrastructure.
Here are five popular open source TSDBs:
Prometheus is an open source monitoring system with a time series database for storing and querying metrics data. It is designed to work well with other cloud native technologies, such as Kubernetes and Docker, and features a powerful query language called PromQL.
InfluxDB’s unified architecture, which combines APIs for storing and querying data, background processing for extract, transform and load (ETL) and monitoring, user dashboards and data visualization, makes it a powerful and versatile tool for handling time series data.
With Kapacitor for background processing and Chronograf for UI, InfluxDB, intially developed by InfluxData, offers a comprehensive solution that can meet the needs of organizations with complex data workflows.
Additionally, InfluxDB’s open source nature allows for customization and integration with other tools, making it a flexible solution that can adapt to changing business needs.
This open source TSDB is designed to handle large-scale time series data generated by IoT devices and industrial applications, including connected cars. Its architecture is optimized for real-time data ingestion, processing and monitoring, allowing it to efficiently handle data sets of terabyte and even petabyte-scale per day.
TDengine’s cloud native architecture makes it highly scalable and flexible, making it an appropriate choice for organizations that require a high-performance, reliable time series database for their IoT applications.
A powerful open source database designed to handle time series data at scale while making SQL scalable. TimescaleDB builds on PostgreSQL, and it is packaged as a PostgreSQL extension, providing automatic partitioning across time and space based on partitioning key.
One of the key benefits of TimescaleDB is its fast ingest capabilities, which make it well-suited for the ingestion of large volumes of time series data. Additionally, TimescaleDB supports complex queries, making it easier to perform advanced data analysis.
QuestDB is an open source TSDB that provides high throughput ingestion and fast SQL queries with operational simplicity. QuestDB supports schema-agnostic ingestion, which means that it can handle data from multiple sources, including the InfluxDB line protocol, PostgreSQL wire protocol, and a REST API for bulk imports and exports.
One of the key benefits of QuestDB is its performance. It is designed to handle large volumes of time series data with low latency and high throughput, making it well-suited for use cases such as financial market data, application metrics, sensor data, real-time analytics, dashboards, and infrastructure monitoring.
Selecting an appropriate time series database for a specific use case requires careful consideration of the data types supported by the database. While some use cases may only require numeric data types, many other scenarios, such as IoT and connected cars, may require Boolean, integer, and string data types, among others.
Choosing a database that supports multiple data types can provide benefits such as enhanced compression ratios and reduced storage costs. However, it is important to also consider the additional complexity and costs associated with using such a database, including data validation, transformation and normalization.
Ultimately, the right TSDB can enable real-time data-driven insights and improve decision-making, while the wrong one can limit an organization’s ability to extract value from its data. As time marches on and the volume of time series data continues to grow, selecting a TSDB that is suited to your organization’s needs will become increasingly crucial.